Explore mouse snRNA-seq matching viseum chips
Vitalii Kleshchevnikov
14/09/2019
Load and plot snRNA-seq data
seu = load_snrna_viseum(nfeatures = 5000, M3Drop_threshold = 0.1, emptyDrops_fdr_p_val = 0.01, scrublet_score = 1,
dir10x = c("filtered_feature_bc_matrix"))## Warning: Transformation introduced infinite values in continuous y-axis## Warning: Removed 27 rows containing missing values (geom_bar).## Warning: Transformation introduced infinite values in continuous y-axis## Warning: Removed 27 rows containing missing values (geom_bar).## Warning: Transformation introduced infinite values in continuous y-axis## Warning: Removed 29 rows containing missing values (geom_bar).## Warning: Transformation introduced infinite values in continuous y-axis## Warning: Removed 29 rows containing missing values (geom_bar).## Warning: Transformation introduced infinite values in continuous y-axis## Warning: Removed 31 rows containing missing values (geom_bar).## Warning: Transformation introduced infinite values in continuous y-axis## Warning: Removed 25 rows containing missing values (geom_bar).
## [1] "Removing 0 undetected genes."
## NULL## snapshotDate(): 2019-05-02## downloading 0 resources## loading from cache
## 'AH70573 : 77319'
## Centering and scaling data matrix## PC_ 1
## Positive: ENSMUSG00000025551, ENSMUSG00000021268, ENSMUSG00000041014, ENSMUSG00000060924, ENSMUSG00000079157
## Negative: ENSMUSG00000062078, ENSMUSG00000031425, ENSMUSG00000032841, ENSMUSG00000033740, ENSMUSG00000041607
## PC_ 2
## Positive: ENSMUSG00000069670, ENSMUSG00000037996, ENSMUSG00000026109, ENSMUSG00000028399, ENSMUSG00000027674
## Negative: ENSMUSG00000007097, ENSMUSG00000022112, ENSMUSG00000014361, ENSMUSG00000039835, ENSMUSG00000048960
## PC_ 3
## Positive: ENSMUSG00000026288, ENSMUSG00000035697, ENSMUSG00000020709, ENSMUSG00000052085, ENSMUSG00000048163
## Negative: ENSMUSG00000021010, ENSMUSG00000007097, ENSMUSG00000022112, ENSMUSG00000028517, ENSMUSG00000039835
## PC_ 4
## Positive: ENSMUSG00000039607, ENSMUSG00000020042, ENSMUSG00000027985, ENSMUSG00000050315, ENSMUSG00000075316
## Negative: ENSMUSG00000054728, ENSMUSG00000022237, ENSMUSG00000025372, ENSMUSG00000026090, ENSMUSG00000074968
## PC_ 5
## Positive: ENSMUSG00000027985, ENSMUSG00000038764, ENSMUSG00000050315, ENSMUSG00000021948, ENSMUSG00000048332
## Negative: ENSMUSG00000055761, ENSMUSG00000053519, ENSMUSG00000110246, ENSMUSG00000031559, ENSMUSG00000059493
## Computing nearest neighbor graph## Computing SNN## Modularity Optimizer version 1.3.0 by Ludo Waltman and Nees Jan van Eck
##
## Number of nodes: 44418
## Number of edges: 2014276
##
## Running Louvain algorithm...
## Maximum modularity in 10 random starts: 0.8784
## Number of communities: 87
## Elapsed time: 12 seconds## 09:06:17 Read 44418 rows and found 75 numeric columns## 09:06:17 Using Annoy for neighbor search, n_neighbors = 30## 09:06:17 Building Annoy index with metric = cosine, n_trees = 50## 0% 10 20 30 40 50 60 70 80 90 100%## [----|----|----|----|----|----|----|----|----|----|## **************************************************|
## 09:06:28 Writing NN index file to temp file /tmp/Rtmpwpt6jn/file1dfb31616635
## 09:06:28 Searching Annoy index using 1 thread, search_k = 3000
## 09:06:49 Annoy recall = 100%
## 09:06:52 Commencing smooth kNN distance calibration using 1 thread
## 09:06:59 Initializing from normalized Laplacian + noise
## 09:07:06 Commencing optimization for 200 epochs, with 2031356 positive edges
## 09:07:58 Optimization finished
seu = FindClusters(seu, resolution = 0.1, n.start = 10)## Modularity Optimizer version 1.3.0 by Ludo Waltman and Nees Jan van Eck
##
## Number of nodes: 44418
## Number of edges: 2014276
##
## Running Louvain algorithm...
## Maximum modularity in 10 random starts: 0.9871
## Number of communities: 31
## Elapsed time: 9 secondspoint_size=0.1
# show cells
p1 = DimPlot(seu, reduction = "umap", pt.size = point_size, group.by = "RNA_snn_res.0.1") +
ggtitle(label = "seurat_clusters")
p2 = DimPlot(seu, reduction = "umap", pt.size = point_size, group.by = "sample") +
ggtitle(label = "sample")
p3 = FeaturePlot(seu, features = c("nCount_RNA"),
reduction = "umap", pt.size = point_size, slot = "counts") +
ggtitle(label = "nUMI") +
scale_colour_viridis_c(trans = "log10", option = "magma",
breaks = c(500, 1000, 10000, 100000), labels = c("500", "1k", "10k", "100k"))## Scale for 'colour' is already present. Adding another scale for
## 'colour', which will replace the existing scale.p4 = FeaturePlot(seu, features = c("nFeature_RNA"),
reduction = "umap", pt.size = point_size, slot = "counts") +
ggtitle(label = "nGenes") +
scale_colour_viridis_c(trans = "log10", option = "magma",
breaks = c(200, 500, 1000, 2000, 5000, 8000), labels = c("200", "500", "1k", "2k", "5k", "8k"))## Scale for 'colour' is already present. Adding another scale for
## 'colour', which will replace the existing scale.p5 = FeaturePlot(seu, features = c("ENSMUSG00000070570"),
reduction = "umap", pt.size = point_size, slot = "counts") +
ggtitle(label = "Slc17a7 - Vesicular glutamate transporter 1") +
scale_colour_distiller(trans = "log10", palette = "RdPu")## Scale for 'colour' is already present. Adding another scale for
## 'colour', which will replace the existing scale.cowplot::plot_grid(plotlist = list(p1, p2, p3, p4, p5), ncol = 4, align = "hv")## Warning: Transformation introduced infinite values in discrete y-axis
Load and plot Zeisel data for matching regions
Matching regions include:
Amygdala, Hippocampus, Hypothalamus, MiddleCortex, Striatum, Thalamus
zeisel = load_zeisel(regions = c('Amygd', 'CA1', 'HC', 'Hypoth', 'Ctx2', 'SScortex', 'StriatVent', 'StriatDor', 'Thal'),
data_dir = "./data/mouse_zeisel/")## Warning: Transformation introduced infinite values in continuous y-axis## Warning: Removed 27 rows containing missing values (geom_bar).## Warning: Transformation introduced infinite values in continuous y-axis## Warning: Removed 33 rows containing missing values (geom_bar).## Warning: Transformation introduced infinite values in continuous y-axis## Warning: Removed 30 rows containing missing values (geom_bar).## Warning: Transformation introduced infinite values in continuous y-axis## Warning: Removed 29 rows containing missing values (geom_bar).## Warning: Transformation introduced infinite values in continuous y-axis## Warning: Removed 28 rows containing missing values (geom_bar).## Warning: Transformation introduced infinite values in continuous y-axis## Warning: Removed 27 rows containing missing values (geom_bar).## Warning: Transformation introduced infinite values in continuous y-axis## Warning: Removed 30 rows containing missing values (geom_bar).## Warning: Transformation introduced infinite values in continuous y-axis## Warning: Removed 30 rows containing missing values (geom_bar).## Warning: Transformation introduced infinite values in continuous y-axis## Warning: Removed 29 rows containing missing values (geom_bar).
## [1] "Removing 0 undetected genes."
## NULL## snapshotDate(): 2019-05-02## downloading 0 resources## loading from cache
## 'AH70572 : 77318'## Warning in SingleExIPlot(type = type, data = data[, x, drop = FALSE],
## idents = idents, : All cells have the same value of percent.mt.## Warning in SingleExIPlot(type = type, data = data[, x, drop = FALSE],
## idents = idents, : All cells have the same value of nucl_mito_genes.
## Centering and scaling data matrix## PC_ 1
## Positive: ENSMUSG00000021268, ENSMUSG00000044349, ENSMUSG00000026576, ENSMUSG00000021087, ENSMUSG00000035864
## Negative: ENSMUSG00000062078, ENSMUSG00000029622, ENSMUSG00000037706, ENSMUSG00000029723, ENSMUSG00000074457
## PC_ 2
## Positive: ENSMUSG00000037625, ENSMUSG00000036634, ENSMUSG00000076439, ENSMUSG00000032854, ENSMUSG00000027858
## Negative: ENSMUSG00000041378, ENSMUSG00000018593, ENSMUSG00000029648, ENSMUSG00000079018, ENSMUSG00000036256
## PC_ 3
## Positive: ENSMUSG00000041378, ENSMUSG00000029648, ENSMUSG00000079018, ENSMUSG00000036256, ENSMUSG00000030413
## Negative: ENSMUSG00000036887, ENSMUSG00000036905, ENSMUSG00000036896, ENSMUSG00000030579, ENSMUSG00000038642
## PC_ 4
## Positive: ENSMUSG00000036887, ENSMUSG00000036905, ENSMUSG00000036896, ENSMUSG00000030579, ENSMUSG00000038642
## Negative: ENSMUSG00000017390, ENSMUSG00000022037, ENSMUSG00000050953, ENSMUSG00000020591, ENSMUSG00000028517
## PC_ 5
## Positive: ENSMUSG00000055430, ENSMUSG00000001988, ENSMUSG00000028832, ENSMUSG00000049866, ENSMUSG00000033061
## Negative: ENSMUSG00000032503, ENSMUSG00000053310, ENSMUSG00000046215, ENSMUSG00000048644, ENSMUSG00000061718
## Computing nearest neighbor graph## Computing SNN## Modularity Optimizer version 1.3.0 by Ludo Waltman and Nees Jan van Eck
##
## Number of nodes: 52572
## Number of edges: 2541260
##
## Running Louvain algorithm...
## Maximum modularity in 10 random starts: 0.9114
## Number of communities: 86
## Elapsed time: 12 seconds## 09:21:22 Read 52572 rows and found 75 numeric columns## 09:21:22 Using Annoy for neighbor search, n_neighbors = 30## 09:21:22 Building Annoy index with metric = cosine, n_trees = 50## 0% 10 20 30 40 50 60 70 80 90 100%## [----|----|----|----|----|----|----|----|----|----|## **************************************************|
## 09:21:35 Writing NN index file to temp file /tmp/Rtmpwpt6jn/file1dfb1df44f0f
## 09:21:35 Searching Annoy index using 1 thread, search_k = 3000
## 09:21:55 Annoy recall = 100%
## 09:21:58 Commencing smooth kNN distance calibration using 1 thread
## 09:22:05 Initializing from normalized Laplacian + noise
## 09:22:16 Commencing optimization for 200 epochs, with 2422702 positive edges
## 09:23:19 Optimization finished## Warning in FeaturePlot(seu, reduction = "umap", pt.size = 0.1, features =
## "percent.mt"): All cells have the same value (0) of percent.mt.
point_size=0.1
# show cells
p1 = DimPlot(zeisel, reduction = "umap", pt.size = point_size, group.by = "Class") +
ggtitle(label = "Annotation")
p2 = DimPlot(zeisel, reduction = "umap", pt.size = point_size, group.by = "Tissue") +
ggtitle(label = "Tissue")
p3 = FeaturePlot(zeisel, features = c("nCount_RNA"),
reduction = "umap", pt.size = point_size, slot = "counts") +
ggtitle(label = "nUMI") +
scale_colour_viridis_c(trans = "log10", option = "magma",
breaks = c(500, 1000, 10000, 100000), labels = c("500", "1k", "10k", "100k"))## Scale for 'colour' is already present. Adding another scale for
## 'colour', which will replace the existing scale.p4 = FeaturePlot(zeisel, features = c("nFeature_RNA"),
reduction = "umap", pt.size = point_size, slot = "counts") +
ggtitle(label = "nGenes") +
scale_colour_viridis_c(trans = "log10", option = "magma",
breaks = c(200, 500, 1000, 2000, 5000, 8000), labels = c("200", "500", "1k", "2k", "5k", "8k"))## Scale for 'colour' is already present. Adding another scale for
## 'colour', which will replace the existing scale.p5 = FeaturePlot(zeisel, features = c("ENSMUSG00000070570"),
reduction = "umap", pt.size = point_size, slot = "counts") +
ggtitle(label = "Slc17a7 - Vesicular glutamate transporter 1") +
scale_colour_distiller(trans = "log10", palette = "RdPu")## Scale for 'colour' is already present. Adding another scale for
## 'colour', which will replace the existing scale.cowplot::plot_grid(plotlist = list(p1, p2, p3, p4, p5), ncol = 4, align = "hv")## Warning: Transformation introduced infinite values in discrete y-axis
Plot some gene pairs
Combine with Zeisel data using Seurat CCA
I follow the default pipeline in https://satijalab.org/seurat/v3.0/integration.html.
anchors = FindIntegrationAnchors(object.list = list(seu, zeisel),
dims = 1:50)## Computing 2000 integration features## Scaling features for provided objects## Finding all pairwise anchors## Running CCA## Merging objects## Finding neighborhoods## Finding anchors## Found 38582 anchors## Filtering anchors## Retained 8434 anchors## Extracting within-dataset neighborsintegrated = IntegrateData(anchorset = anchors,
k.weight = 50, dims = 1:50)## Merging dataset 1 into 2## Extracting anchors for merged samples## Finding integration vectors## Finding integration vector weights## Integrating data# normalised and PCA-project data
# switch to integrated assay. The variable features of this assay are automatically
# set during IntegrateData
DefaultAssay(integrated) = "integrated"
# Run the standard workflow for visualization and clustering
integrated = ScaleData(integrated, verbose = FALSE)
integrated = RunPCA(integrated, npcs = 50, verbose = FALSE)
integrated = RunUMAP(integrated, reduction = "pca", dims = 1:50)## 12:19:33 Read 96990 rows and found 50 numeric columns## 12:19:33 Using Annoy for neighbor search, n_neighbors = 30## 12:19:33 Building Annoy index with metric = cosine, n_trees = 50## 0% 10 20 30 40 50 60 70 80 90 100%## [----|----|----|----|----|----|----|----|----|----|## **************************************************|
## 12:19:54 Writing NN index file to temp file /tmp/Rtmpwpt6jn/file1dfb2fd38a5f
## 12:19:54 Searching Annoy index using 1 thread, search_k = 3000
## 12:20:37 Annoy recall = 100%
## 12:20:39 Commencing smooth kNN distance calibration using 1 thread
## 12:20:48 Initializing from normalized Laplacian + noise
## 12:21:18 Commencing optimization for 200 epochs, with 4649642 positive edges
## 12:23:18 Optimization finishedintegrated$source = NA
integrated$source[!is.na(integrated$Class)] = "Zeisel"
integrated$source[!is.na(integrated$RNA_snn_res.0.1)] = "snRNA-seq Viseum"
ref_annot = paste0(integrated$Tissue,"_", integrated$Class)
ref_annot[ref_annot == "NANA"] = NA
integrated$ref_annot = ref_annotintegrated = FindNeighbors(integrated, reduction = "pca", dims = 1:50)## Computing nearest neighbor graph## Computing SNNintegrated = FindClusters(integrated, resolution = 0.11, n.start = 10)## Modularity Optimizer version 1.3.0 by Ludo Waltman and Nees Jan van Eck
##
## Number of nodes: 96990
## Number of edges: 4963680
##
## Running Louvain algorithm...
## Maximum modularity in 10 random starts: 0.9894
## Number of communities: 28
## Elapsed time: 33 secondstable(Zeisel_annotations = integrated$Class, new_annot = integrated$integrated_snn_res.0.11,
Tissue = integrated$Tissue, useNA = "ifany")## , , Tissue = Amygd
##
## new_annot
## Zeisel_annotations 0 1 2 3 4 5 6 7 8
## Astrocytes 0 476 0 0 0 0 0 0 0
## Ependymal 0 0 0 0 0 0 0 0 0
## Immune 0 0 0 0 0 0 0 235 0
## Neurons 0 1 170 970 10 109 0 0 1231
## Oligos 158 0 0 0 0 0 0 0 0
## Vascular 0 0 0 1 0 0 0 0 0
## <NA> 0 0 0 0 0 0 0 0 0
## new_annot
## Zeisel_annotations 9 10 11 12 13 14 15 16 17
## Astrocytes 0 0 0 0 0 0 0 0 0
## Ependymal 0 0 0 0 0 0 0 0 0
## Immune 0 0 0 0 2 0 0 0 0
## Neurons 1 3 100 0 0 62 0 1 0
## Oligos 0 0 0 0 0 0 38 8 0
## Vascular 0 0 0 0 114 1 0 0 0
## <NA> 0 0 0 0 0 0 0 0 0
## new_annot
## Zeisel_annotations 18 19 20 21 22 23 24 25 26
## Astrocytes 2 0 0 0 0 0 0 0 0
## Ependymal 0 0 0 0 0 0 29 10 0
## Immune 0 108 0 0 0 0 0 0 0
## Neurons 1 0 0 0 0 0 0 2 27
## Oligos 1 0 0 0 0 0 0 0 0
## Vascular 128 0 0 0 0 0 0 0 0
## <NA> 0 0 0 0 0 0 0 0 0
## new_annot
## Zeisel_annotations 27
## Astrocytes 0
## Ependymal 0
## Immune 0
## Neurons 0
## Oligos 0
## Vascular 0
## <NA> 0
##
## , , Tissue = CA1
##
## new_annot
## Zeisel_annotations 0 1 2 3 4 5 6 7 8
## Astrocytes 0 90 0 0 0 0 0 0 0
## Ependymal 0 0 0 0 0 0 0 0 0
## Immune 0 0 0 0 0 0 0 23 0
## Neurons 0 0 42 0 2 3323 0 0 4
## Oligos 133 0 0 0 0 0 0 0 0
## Vascular 0 0 0 0 0 0 0 0 0
## <NA> 0 0 0 0 0 0 0 0 0
## new_annot
## Zeisel_annotations 9 10 11 12 13 14 15 16 17
## Astrocytes 0 0 0 0 0 0 0 0 0
## Ependymal 0 0 0 0 0 0 0 0 0
## Immune 0 0 0 0 0 0 0 0 0
## Neurons 325 3 1428 28 0 15 0 0 0
## Oligos 0 0 0 0 0 0 25 18 0
## Vascular 0 0 0 0 111 0 0 0 0
## <NA> 0 0 0 0 0 0 0 0 0
## new_annot
## Zeisel_annotations 18 19 20 21 22 23 24 25 26
## Astrocytes 0 0 0 0 0 0 0 0 0
## Ependymal 0 0 0 0 0 0 1 0 0
## Immune 0 17 0 0 0 0 0 0 0
## Neurons 0 0 0 0 0 0 0 8 0
## Oligos 0 0 0 0 0 0 0 0 0
## Vascular 19 0 0 0 0 0 0 0 0
## <NA> 0 0 0 0 0 0 0 0 0
## new_annot
## Zeisel_annotations 27
## Astrocytes 0
## Ependymal 0
## Immune 0
## Neurons 0
## Oligos 0
## Vascular 0
## <NA> 0
##
## , , Tissue = Ctx2
##
## new_annot
## Zeisel_annotations 0 1 2 3 4 5 6 7 8
## Astrocytes 0 905 0 0 0 0 0 0 0
## Ependymal 0 0 0 0 0 0 0 0 0
## Immune 0 0 0 0 0 0 0 310 0
## Neurons 0 0 5 0 1048 288 0 0 222
## Oligos 220 0 0 0 0 0 0 0 0
## Vascular 0 0 0 0 0 0 0 0 0
## <NA> 0 0 0 0 0 0 0 0 0
## new_annot
## Zeisel_annotations 9 10 11 12 13 14 15 16 17
## Astrocytes 0 0 0 0 3 0 0 0 0
## Ependymal 0 0 0 0 0 0 0 0 0
## Immune 0 0 0 0 4 0 0 0 0
## Neurons 95 0 122 688 0 0 0 0 0
## Oligos 0 0 0 0 0 0 43 12 0
## Vascular 0 0 0 0 316 0 0 0 0
## <NA> 0 0 0 0 0 0 0 0 0
## new_annot
## Zeisel_annotations 18 19 20 21 22 23 24 25 26
## Astrocytes 0 0 0 0 0 0 0 0 0
## Ependymal 0 0 0 0 0 0 0 0 0
## Immune 0 63 0 0 0 0 0 0 0
## Neurons 0 0 0 0 0 130 0 0 0
## Oligos 0 0 0 0 0 0 0 0 0
## Vascular 48 0 0 0 0 0 0 0 0
## <NA> 0 0 0 0 0 0 0 0 0
## new_annot
## Zeisel_annotations 27
## Astrocytes 0
## Ependymal 0
## Immune 0
## Neurons 0
## Oligos 0
## Vascular 0
## <NA> 0
##
## , , Tissue = HC
##
## new_annot
## Zeisel_annotations 0 1 2 3 4 5 6 7 8
## Astrocytes 0 1707 0 0 0 0 0 0 0
## Ependymal 0 0 0 0 0 0 0 0 0
## Immune 0 0 0 0 0 0 0 300 0
## Neurons 0 2 4 2 2 124 0 0 65
## Oligos 358 0 0 0 0 0 0 0 0
## Vascular 0 0 0 0 0 0 0 0 0
## <NA> 0 0 0 0 0 0 0 0 0
## new_annot
## Zeisel_annotations 9 10 11 12 13 14 15 16 17
## Astrocytes 0 4 0 0 2 1 0 0 0
## Ependymal 0 0 0 0 0 0 0 0 0
## Immune 0 0 0 0 0 0 0 0 0
## Neurons 2024 2051 32 34 2 785 0 4 0
## Oligos 0 0 0 0 0 0 155 79 0
## Vascular 0 0 0 0 255 0 0 0 0
## <NA> 0 0 0 0 0 0 0 0 0
## new_annot
## Zeisel_annotations 18 19 20 21 22 23 24 25 26
## Astrocytes 1 0 0 0 0 0 0 0 0
## Ependymal 0 0 0 0 0 0 21 0 0
## Immune 0 83 0 0 0 0 0 0 0
## Neurons 2 0 0 0 0 0 1 89 0
## Oligos 0 0 0 0 0 0 0 0 0
## Vascular 70 0 0 0 0 0 0 0 0
## <NA> 0 0 0 0 0 0 0 0 0
## new_annot
## Zeisel_annotations 27
## Astrocytes 0
## Ependymal 0
## Immune 0
## Neurons 0
## Oligos 0
## Vascular 0
## <NA> 0
##
## , , Tissue = Hypoth
##
## new_annot
## Zeisel_annotations 0 1 2 3 4 5 6 7 8
## Astrocytes 0 974 0 0 0 0 0 0 0
## Ependymal 0 3 1 0 0 0 0 0 0
## Immune 0 0 0 0 0 0 0 256 0
## Neurons 0 2 2130 2 0 0 37 0 0
## Oligos 794 0 1 0 0 0 0 0 0
## Vascular 0 0 0 0 0 0 0 0 0
## <NA> 0 0 0 0 0 0 0 0 0
## new_annot
## Zeisel_annotations 9 10 11 12 13 14 15 16 17
## Astrocytes 0 0 0 0 3 0 0 0 0
## Ependymal 0 0 0 0 0 1 0 0 0
## Immune 0 0 0 0 0 0 0 0 0
## Neurons 0 0 4 0 5 0 0 0 0
## Oligos 0 0 0 0 0 0 358 50 0
## Vascular 0 0 0 0 376 0 0 0 0
## <NA> 0 0 0 0 0 0 0 0 0
## new_annot
## Zeisel_annotations 18 19 20 21 22 23 24 25 26
## Astrocytes 0 0 0 0 0 0 0 0 0
## Ependymal 0 0 0 0 0 0 63 5 0
## Immune 0 128 0 0 0 0 0 0 0
## Neurons 0 0 34 0 1 0 0 0 124
## Oligos 0 0 0 0 0 0 0 0 0
## Vascular 132 0 0 0 0 0 0 0 0
## <NA> 0 0 0 0 0 0 0 0 0
## new_annot
## Zeisel_annotations 27
## Astrocytes 0
## Ependymal 0
## Immune 0
## Neurons 0
## Oligos 0
## Vascular 0
## <NA> 0
##
## , , Tissue = SScortex
##
## new_annot
## Zeisel_annotations 0 1 2 3 4 5 6 7 8
## Astrocytes 0 144 0 0 0 0 0 0 0
## Ependymal 0 0 0 0 0 0 0 0 0
## Immune 0 0 0 0 0 0 0 215 0
## Neurons 0 0 1 0 862 43 0 0 2
## Oligos 208 0 0 0 0 0 0 0 0
## Vascular 0 0 0 0 0 0 0 0 0
## <NA> 0 0 0 0 0 0 0 0 0
## new_annot
## Zeisel_annotations 9 10 11 12 13 14 15 16 17
## Astrocytes 0 0 0 0 0 0 0 0 0
## Ependymal 0 0 0 0 0 0 0 0 0
## Immune 0 0 0 0 3 0 0 0 0
## Neurons 5 0 3 147 1 0 0 0 0
## Oligos 0 0 0 0 0 0 41 59 0
## Vascular 0 0 0 0 309 0 0 0 0
## <NA> 0 0 0 0 0 0 0 0 0
## new_annot
## Zeisel_annotations 18 19 20 21 22 23 24 25 26
## Astrocytes 0 0 0 0 0 0 0 0 0
## Ependymal 0 0 0 0 0 0 0 0 0
## Immune 0 57 0 0 0 0 0 0 0
## Neurons 1 0 0 0 0 16 0 0 0
## Oligos 0 0 0 0 0 0 0 0 0
## Vascular 146 0 0 0 0 0 0 0 0
## <NA> 0 0 0 0 0 0 0 0 0
## new_annot
## Zeisel_annotations 27
## Astrocytes 0
## Ependymal 0
## Immune 0
## Neurons 0
## Oligos 0
## Vascular 0
## <NA> 0
##
## , , Tissue = StriatDor
##
## new_annot
## Zeisel_annotations 0 1 2 3 4 5 6 7 8
## Astrocytes 0 668 0 0 0 0 0 0 0
## Ependymal 0 0 0 0 0 0 0 0 0
## Immune 0 0 0 0 0 0 0 337 0
## Neurons 0 1 74 1926 0 1 0 0 0
## Oligos 541 0 0 1 0 0 0 0 0
## Vascular 0 0 0 0 0 0 0 0 0
## <NA> 0 0 0 0 0 0 0 0 0
## new_annot
## Zeisel_annotations 9 10 11 12 13 14 15 16 17
## Astrocytes 0 0 0 0 0 3 0 0 0
## Ependymal 0 0 0 0 0 0 0 0 0
## Immune 0 0 0 0 0 0 0 0 0
## Neurons 0 0 91 0 1 429 0 0 0
## Oligos 0 0 0 0 0 0 63 66 0
## Vascular 0 0 0 0 174 1 0 0 0
## <NA> 0 0 0 0 0 0 0 0 0
## new_annot
## Zeisel_annotations 18 19 20 21 22 23 24 25 26
## Astrocytes 0 0 0 0 0 0 0 0 0
## Ependymal 0 0 0 0 0 0 67 0 0
## Immune 0 59 0 0 0 0 0 0 0
## Neurons 0 0 0 0 0 0 0 0 20
## Oligos 1 0 0 0 0 0 0 0 0
## Vascular 69 0 0 0 0 0 0 0 0
## <NA> 0 0 0 0 0 0 0 0 0
## new_annot
## Zeisel_annotations 27
## Astrocytes 0
## Ependymal 0
## Immune 0
## Neurons 0
## Oligos 0
## Vascular 0
## <NA> 0
##
## , , Tissue = StriatVent
##
## new_annot
## Zeisel_annotations 0 1 2 3 4 5 6 7 8
## Astrocytes 0 775 0 0 0 0 0 0 0
## Ependymal 0 0 0 0 0 0 0 0 0
## Immune 0 0 0 0 0 0 0 430 0
## Neurons 0 3 1471 2805 92 12 0 1 133
## Oligos 511 1 0 0 0 0 0 0 0
## Vascular 0 0 0 0 0 0 0 0 0
## <NA> 0 0 0 0 0 0 0 0 0
## new_annot
## Zeisel_annotations 9 10 11 12 13 14 15 16 17
## Astrocytes 0 0 0 0 0 2 0 0 0
## Ependymal 0 0 0 0 0 0 0 0 0
## Immune 0 0 0 0 1 0 0 0 0
## Neurons 5 0 55 42 12 471 0 4 0
## Oligos 0 0 0 0 0 0 98 42 0
## Vascular 0 0 0 0 231 0 0 0 0
## <NA> 0 0 0 0 0 0 0 0 0
## new_annot
## Zeisel_annotations 18 19 20 21 22 23 24 25 26
## Astrocytes 0 0 0 0 0 0 0 0 0
## Ependymal 0 0 0 0 0 0 67 0 0
## Immune 0 80 0 0 0 0 0 0 0
## Neurons 1 0 3 0 0 13 0 2 62
## Oligos 0 0 0 0 0 0 0 0 0
## Vascular 87 0 0 0 0 0 0 0 0
## <NA> 0 0 0 0 0 0 0 0 0
## new_annot
## Zeisel_annotations 27
## Astrocytes 0
## Ependymal 0
## Immune 0
## Neurons 0
## Oligos 0
## Vascular 0
## <NA> 0
##
## , , Tissue = Thal
##
## new_annot
## Zeisel_annotations 0 1 2 3 4 5 6 7 8
## Astrocytes 0 942 0 0 0 0 0 0 0
## Ependymal 0 0 0 0 0 0 0 0 0
## Immune 0 0 0 0 0 0 0 300 0
## Neurons 0 0 751 0 0 0 2306 0 0
## Oligos 3167 1 0 0 0 0 0 0 0
## Vascular 0 0 0 0 0 0 0 0 0
## <NA> 0 0 0 0 0 0 0 0 0
## new_annot
## Zeisel_annotations 9 10 11 12 13 14 15 16 17
## Astrocytes 0 0 0 0 0 0 0 0 0
## Ependymal 0 0 0 0 0 0 0 0 0
## Immune 0 0 0 0 2 4 0 0 0
## Neurons 0 0 1 0 6 0 0 2 0
## Oligos 0 0 0 0 0 0 612 156 0
## Vascular 0 0 0 0 785 0 0 0 0
## <NA> 0 0 0 0 0 0 0 0 0
## new_annot
## Zeisel_annotations 18 19 20 21 22 23 24 25 26
## Astrocytes 2 0 0 0 0 0 0 0 0
## Ependymal 0 0 0 0 0 0 76 48 0
## Immune 0 165 0 0 0 0 0 0 0
## Neurons 0 0 467 208 98 0 1 7 0
## Oligos 2 0 0 0 0 0 0 0 0
## Vascular 216 0 0 0 0 0 0 0 0
## <NA> 0 0 0 0 0 0 0 0 0
## new_annot
## Zeisel_annotations 27
## Astrocytes 0
## Ependymal 0
## Immune 0
## Neurons 0
## Oligos 0
## Vascular 0
## <NA> 0
##
## , , Tissue = NA
##
## new_annot
## Zeisel_annotations 0 1 2 3 4 5 6 7 8
## Astrocytes 0 0 0 0 0 0 0 0 0
## Ependymal 0 0 0 0 0 0 0 0 0
## Immune 0 0 0 0 0 0 0 0 0
## Neurons 0 0 0 0 0 0 0 0 0
## Oligos 0 0 0 0 0 0 0 0 0
## Vascular 0 0 0 0 0 0 0 0 0
## <NA> 11792 3228 4079 1873 4165 981 2132 1855 2566
## new_annot
## Zeisel_annotations 9 10 11 12 13 14 15 16 17
## Astrocytes 0 0 0 0 0 0 0 0 0
## Ependymal 0 0 0 0 0 0 0 0 0
## Immune 0 0 0 0 0 0 0 0 0
## Neurons 0 0 0 0 0 0 0 0 0
## Oligos 0 0 0 0 0 0 0 0 0
## Vascular 0 0 0 0 0 0 0 0 0
## <NA> 1545 1663 1490 2207 73 389 328 1053 1047
## new_annot
## Zeisel_annotations 18 19 20 21 22 23 24 25 26
## Astrocytes 0 0 0 0 0 0 0 0 0
## Ependymal 0 0 0 0 0 0 0 0 0
## Immune 0 0 0 0 0 0 0 0 0
## Neurons 0 0 0 0 0 0 0 0 0
## Oligos 0 0 0 0 0 0 0 0 0
## Vascular 0 0 0 0 0 0 0 0 0
## <NA> 60 69 233 481 454 309 4 121 12
## new_annot
## Zeisel_annotations 27
## Astrocytes 0
## Ependymal 0
## Immune 0
## Neurons 0
## Oligos 0
## Vascular 0
## <NA> 209table(Zeisel_annotations = integrated$Class, new_annot = integrated$integrated_snn_res.0.11,
useNA = "ifany")## new_annot
## Zeisel_annotations 0 1 2 3 4 5 6 7 8
## Astrocytes 0 6681 0 0 0 0 0 0 0
## Ependymal 0 3 1 0 0 0 0 0 0
## Immune 0 0 0 0 0 0 0 2406 0
## Neurons 0 9 4648 5705 2016 3900 2343 1 1657
## Oligos 6090 2 1 1 0 0 0 0 0
## Vascular 0 0 0 1 0 0 0 0 0
## <NA> 11792 3228 4079 1873 4165 981 2132 1855 2566
## new_annot
## Zeisel_annotations 9 10 11 12 13 14 15 16 17
## Astrocytes 0 4 0 0 8 6 0 0 0
## Ependymal 0 0 0 0 0 1 0 0 0
## Immune 0 0 0 0 12 4 0 0 0
## Neurons 2455 2057 1836 939 27 1762 0 11 0
## Oligos 0 0 0 0 0 0 1433 490 0
## Vascular 0 0 0 0 2671 2 0 0 0
## <NA> 1545 1663 1490 2207 73 389 328 1053 1047
## new_annot
## Zeisel_annotations 18 19 20 21 22 23 24 25 26
## Astrocytes 5 0 0 0 0 0 0 0 0
## Ependymal 0 0 0 0 0 0 324 63 0
## Immune 0 760 0 0 0 0 0 0 0
## Neurons 5 0 504 208 99 159 2 108 233
## Oligos 4 0 0 0 0 0 0 0 0
## Vascular 915 0 0 0 0 0 0 0 0
## <NA> 60 69 233 481 454 309 4 121 12
## new_annot
## Zeisel_annotations 27
## Astrocytes 0
## Ependymal 0
## Immune 0
## Neurons 0
## Oligos 0
## Vascular 0
## <NA> 209table(new_annot = integrated$integrated_snn_res.0.11,
Tissue = integrated$Tissue, useNA = "ifany")## Tissue
## new_annot Amygd CA1 Ctx2 HC Hypoth SScortex StriatDor StriatVent
## 0 158 133 220 358 794 208 541 511
## 1 477 90 905 1709 979 144 669 779
## 2 170 42 5 4 2132 1 74 1471
## 3 971 0 0 2 2 0 1927 2805
## 4 10 2 1048 2 0 862 0 92
## 5 109 3323 288 124 0 43 1 12
## 6 0 0 0 0 37 0 0 0
## 7 235 23 310 300 256 215 337 431
## 8 1231 4 222 65 0 2 0 133
## 9 1 325 95 2024 0 5 0 5
## 10 3 3 0 2055 0 0 0 0
## 11 100 1428 122 32 4 3 91 55
## 12 0 28 688 34 0 147 0 42
## 13 116 111 323 259 384 313 175 244
## 14 63 15 0 786 1 0 433 473
## 15 38 25 43 155 358 41 63 98
## 16 9 18 12 83 50 59 66 46
## 17 0 0 0 0 0 0 0 0
## 18 132 19 48 73 132 147 70 88
## 19 108 17 63 83 128 57 59 80
## 20 0 0 0 0 34 0 0 3
## 21 0 0 0 0 0 0 0 0
## 22 0 0 0 0 1 0 0 0
## 23 0 0 130 0 0 16 0 13
## 24 29 1 0 22 63 0 67 67
## 25 12 8 0 89 5 0 0 2
## 26 27 0 0 0 124 0 20 62
## 27 0 0 0 0 0 0 0 0
## Tissue
## new_annot Thal <NA>
## 0 3167 11792
## 1 943 3228
## 2 751 4079
## 3 0 1873
## 4 0 4165
## 5 0 981
## 6 2306 2132
## 7 300 1855
## 8 0 2566
## 9 0 1545
## 10 0 1663
## 11 1 1490
## 12 0 2207
## 13 793 73
## 14 4 389
## 15 612 328
## 16 158 1053
## 17 0 1047
## 18 220 60
## 19 165 69
## 20 467 233
## 21 208 481
## 22 98 454
## 23 0 309
## 24 77 4
## 25 55 121
## 26 0 12
## 27 0 209saveRDS(integrated, "./data/mouse_viseum_snrna/zeisel_integrated.rds")
fwrite(as.data.table(integrated@meta.data), "./data/mouse_viseum_snrna/zeisel_integrated_meta.tsv", sep = "\t")point_size=0.1
# show cells
p1 = DimPlot(integrated, reduction = "umap", pt.size = point_size, order = TRUE, group.by = "source") +
ggtitle(label = "source")
p2 = DimPlot(integrated, reduction = "umap", pt.size = point_size, order = TRUE, na.value = "grey90", group.by = "RNA_snn_res.0.1") +
ggtitle(label = "seurat_clusters")
p7 = DimPlot(integrated, reduction = "umap", pt.size = point_size, order = TRUE, na.value = "grey90", group.by = "integrated_snn_res.0.11") +
ggtitle(label = "seurat_clusters2")
p3 = DimPlot(integrated, reduction = "umap", pt.size = point_size, order = TRUE, na.value = "grey90", group.by = "Class") +
ggtitle(label = "Zeisel annotations")
p4 = DimPlot(integrated, reduction = "umap", pt.size = point_size, order = TRUE, na.value = "grey90", group.by = "Tissue") +
ggtitle(label = "Zeisel region")
p5 = FeaturePlot(integrated, features = c("nCount_RNA"), order = TRUE,
reduction = "umap", pt.size = point_size, slot = "counts") +
ggtitle(label = "nUMI") +
scale_colour_viridis_c(trans = "log10", option = "magma",
breaks = c(500, 1000, 10000, 100000), labels = c("500", "1k", "10k", "100k"))## Scale for 'colour' is already present. Adding another scale for
## 'colour', which will replace the existing scale.p6 = FeaturePlot(integrated, features = c("nFeature_RNA"), order = TRUE,
reduction = "umap", pt.size = point_size, slot = "counts") +
ggtitle(label = "nGenes") +
scale_colour_viridis_c(trans = "log10", option = "magma",
breaks = c(200, 500, 1000, 2000, 5000, 8000), labels = c("200", "500", "1k", "2k", "5k", "8k"))## Scale for 'colour' is already present. Adding another scale for
## 'colour', which will replace the existing scale.p7 = FeaturePlot(integrated, features = c("ENSMUSG00000070570"), order = TRUE,
reduction = "umap", pt.size = point_size, slot = "counts") +
ggtitle(label = "Slc17a7 - Vesicular glutamate transporter 1") +
scale_colour_distiller(trans = "log10", palette = "RdPu")## Warning: Could not find ENSMUSG00000070570 in the default search locations,
## found in RNA assay instead## Scale for 'colour' is already present. Adding another scale for
## 'colour', which will replace the existing scale.cowplot::plot_grid(plotlist = list(p1, p2, p7, p3, p4, p5, p6, p7), ncol = 4, align = "hv")## Warning: Transformation introduced infinite values in discrete y-axis## Warning: Transformation introduced infinite values in discrete y-axis
Transfer cell labels
trans_anchors = FindTransferAnchors(reference = zeisel, query = seu,
dims = 1:50)
predictions = TransferData(anchorset = anchors, refdata = zeisel$ref_annot,
dims = 1:50)
seu = AddMetaData(seu, metadata = predictions)cat = data.table(c("Cell source", "source"),
c("Zeisel annotations", "Class"),
c("snRNA-seq Viseum annotations", "annot"))
plot_markers(integrated, markers = my_neuro_markers, categ = cat,
point_size = 0.3, slot = "data", # log scale here for comparability between datasets
ncol = 6)